Discover the worst problems, prioritize, and rapidly optimize your AI applications.
We recently moved from a popular OSS framework to AIMon for its accuracy and latency benefits.
The productivity gains provided by LLMs are only as valuable as the trust in the LLMs’ output. Reliability tools like AIMon are key to enabling that business value. That is critical to professionals in fields like security compliance where programs are looking to drive adoption of these tools and use them as force multipliers.
AIMon provided us with comprehensive visibility into our entire LLM-RAG stack, clearly highlighting accuracy issues that we hadn't previously identified. Its robust evaluation models enabled us to pinpoint exactly where improvements were needed, significantly enhancing the quality and reliability of our RAG and LLM outputs.
AIMon's Hallucination and Adherence models have become essential components of our technology stack. They enable us to identify issues with our LLM outputs and fine-tune our models for optimal performance.
Monitor your internally-built apps and your AI vendors too
AIMon can monitor your internal RAG, LLM, Agentic apps AND your AI vendors too.
Seamlessly observe production and development workflows
With AIMon's continuous monitoring, you don't need to restrict yourself to evaluating offline. You can get live insights that help you optimize your apps.
Deploy AIMon hosted or on-premise
AIMon can be deployed on-premise or hosted in the cloud to suit your company's trust policies.
Find out why Fortune 200 companies trust us.
Output / Hallucination
Identify phrase-level, contextual, and general-knowledge hallucination scores better than GPT-4o in a few hundred milliseconds.
Read moreOutput / Instruction Adherence
Check if your LLMs deviate from your instructions and why. 87%+ accuracy and <500ms latency.
Read moreRAG / Context Issues
Identify context quality issues like conflicting information to troubleshoot and fix root causes of LLM hallucinations.
Read moreRAG / Context Relevance and Reranking
Determine the query-context relevance scores for your retrievals with a model that ranks in the top 5 on the MTEB leaderboard. Use the feedback and rerank your retrievals with our reranker.
Read moreOutput / Completeness and Conciseness
Check if your LLMs captured all the important information expected or when they talked too much.
Read moreOutput / Toxicity and Bias
Detect hate speech, obscenities, discriminatory language, bias, and more.
Read more
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Explore our GitHub and NPM pages for ready-made example apps. Starting to use AIMon takes 15 minutes.
Check out the Docs
Review examples and recipes that help you improve your apps.
Integrate AIMon or Use without Code
Unlock instant or offline insights into your LLM apps with our powerful SDKs, API, or simply use our UI with your dataset.
Evaluate, Monitor, and Optimize
Find top problematic LLM Apps, identify quality issues and gain critical insights to optimize effectively.
Arxiv Publication - HalluciNot, Hallucination Detection Through Context and Common Knowledge Verification
How AIMon's Benchmark-leading "Checker Models" outshine LLMs for evaluation and monitoring.
How to improve RAG Relevance by over 100% and overall output quality by 30% in your RAG and LLM Apps with AIMon.
How to build Accuracy Flywheels for your LLM/RAG Apps. And a demo of how to detect Hallucinations with AIMon.
